Holocene hydroclimatic variability recorded in sediments from Maddox Lake (northern California Coast Range)
Bibliographic record
Abstract
Abstract Perspectives on past climate using lake sediments are critical for assessing modern and future climate change. These perspectives are especially important for water-stressed regions such as the western United States. One such region is northwestern California (CA), where Holocene-length hydroclimatic records are scarce. Here, we present a 9000-year, relative lake level record from Maddox Lake (CA) using a multi-indicator approach. The Early Holocene is characterized by variably low lake levels with a brief excursion to wetter climates/relative highstand ca. 8.4–8.06 cal ka BP, possibly related to the 8.2 ka cold event and changing Atlantic Meridional Overturning Circulation (AMOC). From 5.2–0.55 cal ka BP, Maddox Lake experienced a long-term regression, tracking changes in summer-winter insolation, tropical and northeast Pacific SSTs, and the southward migration of the ITCZ. This gradual regression culminated in a pronounced relative lowstand during the Medieval Climatic Anomaly (MCA). A marked relative highstand followed the MCA, correlative to the Little Ice Age. The latter reflects a far-field response to North Atlantic volcanism, solar variability, and possibly changes in AMOC and Arctic sea ice extent. Our results further confirm the hydroclimatic sensitivity of northwest California to various forcings including those emanating from the North Atlantic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".